The added value of genetic information in colorectal cancer risk prediction models: development and evaluation in the UK Biobank prospective cohort study

Smith, Todd; Gunter, Marc J.; Tzoulaki, Ioanna; Muller, David C.

doi:10.1038/s41416-018-0282-8

Cited by 22 publications

(33 citation statements)

References 12 publications

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“…To gauge the potential public health impact of applying such risk prediction model in the general population, we estimated the 10-year absolute risk of the general UK population ( Figure S3, Most previous efforts mainly focused on the predictive ability of PRS to capture the overall risk of CRC. 4,5,[22][23][24] However, there is compelling evidence suggesting that genetic risk factors may differ by anatomic locations. 25 We therefore aimed to improve prediction of sitespecific CRC by deconstructing the commonly used genomic risk score into several regional scores, allowing susceptibility signals through multiple/different mechanisms to influence genetic predisposition to site-specific CRC.…”

Section: Resultsmentioning

confidence: 99%

“…Hsu et al developed sex‐specific models by using family history and 27 common genetic variants with adjustment of endoscopy history and obtained a discrimination ability of 0.59 for men and 0.56 for women 22 . Similarly, Smith et al reported a c‐statistic of 0.57 for genetic risk model combing 41 CRC susceptibility SNPs 23 . The most recent genetic model for CRC was developed by Jeon et al including 63 CRC susceptibility SNPs and achieved a slightly improved predictive accuracy with a c‐statistic of 0.59 24 .…”

Section: Discussionmentioning

confidence: 99%

“…Most previous efforts mainly focused on the predictive ability of PRS to capture the overall risk of CRC 4,5,22‐24 . However, there is compelling evidence suggesting that genetic risk factors may differ by anatomic locations 25 .…”

Section: Discussionmentioning

confidence: 99%

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Prediction of colorectal cancer risk based on profiling with common genetic variants

Timofeeva

Spiliopoulou

et al. 2020

Intl Journal of Cancer

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Increasing numbers of common genetic variants associated with colorectal cancer (CRC) have been identified. Our study aimed to determine whether risk prediction based on common genetic variants might enable stratification for CRC risk. Metaanalysis of 11 genome-wide association studies comprising 16 871 cases and 26 328 controls was performed to capture CRC susceptibility variants. Genetic prediction models with several candidate polygenic risk scores (PRSs) were generated from Scottish CRC case-control studies (6478 cases and 11 043 controls) and the score with the best performance was then tested in UK Biobank (UKBB) (4800 cases and 20 287 controls). A weighted PRS of 116 CRC single nucleotide polymorphisms (wPRS 116) was found with the best predictive performance, reporting a c-statistics of 0.60 and an odds ratio (OR) of 1.46 (95% confidence interval [CI] = 1.41-1.50, per SD increase) in Scottish data set. The predictive performance of this wPRS 116 was consistently validated in UKBB data set with c-statistics of 0.61 and an OR of 1.49 (95% CI = 1.44-1.54, per SD increase). Modeling the levels of PRS with age and sex in the general UK population shows that employing genetic risk profiling can achieve a moderate degree of risk discrimination that could be helpful to identify a subpopulation with higher CRC risk due to genetic susceptibility.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Prediction of colorectal cancer risk based on profiling with common genetic variants

Timofeeva

Spiliopoulou

et al. 2020

Intl Journal of Cancer

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“…Most previous efforts mainly focused on the predictive ability of PRS to capture the overall risk of CRC. 5,6,[23][24][25] However, there is compelling evidence suggesting that genetic risk factors may differ by anatomic locations. 26,27 We therefore specifically aimed to improve prediction of site-specific CRC by deconstructing the commonly used genomic risk score into several regional scores, allowing susceptibility signals through multiple/different mechanisms to influence genetic predisposition to site-specific CRC.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Colorectal Cancer Risk Based on Profiling with Common Genetic Variants

Timofeeva

Spiliopoulou

et al. 2019

Preprint

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Background: Stratifying the risk of colorectal cancer (CRC) based on polygenic risk scores (PRSs) within populations has the potential to optimize screening and develop targeted prevention strategies. Methods: A metaanalysis of eleven genome wide association studies (GWAS), comprising 16 871 cases and 26 328 controls, was performed to capture CRC susceptibility variants. Genetic models with several candidate PRSs were generated from Scottish CRC case control studies (6478 cases and 11 043 controls) for prediction of overall and site specific CRC. Model performance was validated in UK Biobank (4800 cases and 20 287 controls). The 10-year absolute risk of CRC was estimated by modelling PRS with age and sex using the CRC incidence and mortality rates in the UK population. Findings: A weighted PRS including 116 CRC SNPs (wPRS116) showed the strongest performance. Deconstructing the PRS into multiple genetic risk regional scores or inclusion of additional SNPs that did not reach genome wide significance did not provide any further improvement on predictive performance. The odds ratio (OR) for CRC risk per SD of wPRS116 in Scottish dataset was 1.46 (95%CI: 1.41 to 1.50, c statistics: 0.603). Consistent estimates were observed in UK Biobank (OR=1.49, 95%CI: 1.44 to 1.54, c statistics: 0.610) and showed no substantial heterogeneity among tumor sites. Compared to the middle quintile, those in the highest 1% of PRSs had 3.25 fold higher risk and those in the lowest 1% had 0.32 fold lower risk of developing CRC. Modelling PRS with age and sex in the general UK population allows the identification of a high risk group with 10 year absolute risk >5%. Interpretation: By optimizing wPRS116, we show that genetic factors increase predictive performance but this increment is equivalent to the extraction of only one tenth of the genetic susceptibility. When employing genetic risk profiling in population settings it provides a degree of risk discrimination that could, in principle, be integrated into population based screening programs.

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“…However, risk stratification was not formally evaluated. Considering cancerspecific studies, the PRS presented here achieved superior prediction performance for some cancers 19,20,21,22 , but not others 23,24 . For pancreatic cancer 25 and melanoma 26 , our results are consistent with previous analyses using PRS of similar composition.…”

Section: Discussionmentioning

confidence: 87%

Pan-cancer analysis demonstrates that integrating polygenic risk scores with modifiable risk factors improves risk prediction

Kachuri¹,

Graff²,

Smith‐Byrne

et al. 2020

Preprint

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Cancer risk is determined by a complex interplay of genetic and modifiable risk factors. Combining individual germline risk variants into polygenic risk scores (PRS) creates a personalized genetic susceptibility profile that can be leveraged for disease prediction. Using data from the UK Biobank cohort (413,753 individuals; 22,755 incident cases), we systematically quantify the added predictive value of augmenting conventional cancer risk factors with PRS for 16 cancer types. Our results indicate that incorporating PRS in addition to family history of cancer and modifiable risk factors improves prediction accuracy, but the magnitude of incremental improvement varies substantially between cancers. We also demonstrate the utility of PRS for risk stratification. Individuals with high genetic risk (PRS≥80 th percentile) have significantly divergent 5-year absolute risk trajectories across strata based on family history and modifiable risk factors. Finally, we estimate that high genetic risk accounts for 4.0% to 30.3% of new cancer cases, which exceeds the impact of many lifestyle-related risk factors. In summary, we provide novel quantitative data illustrating the importance of integrating PRS into personalized cancer risk assessment.

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The added value of genetic information in colorectal cancer risk prediction models: development and evaluation in the UK Biobank prospective cohort study

Cited by 22 publications

References 12 publications

Prediction of colorectal cancer risk based on profiling with common genetic variants

Prediction of colorectal cancer risk based on profiling with common genetic variants

Prediction of Colorectal Cancer Risk Based on Profiling with Common Genetic Variants

Pan-cancer analysis demonstrates that integrating polygenic risk scores with modifiable risk factors improves risk prediction

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